354 1. Context and problem statement Mining is entering a decade in which demand for critical raw materials will coexist with stronger expectations on environmental performance, water stewardship, and social legitimacy. At the same time, many operations face declining ore grades, which increases material handled per unit of metal produced and amplifies energy and waste management burdens (Calvo et al., 2016). These pressures make “mining for the future” inseparable from the ability to recover useful outputs from streams historically treated as waste, and to do so in ways that are technically reliable, economically defensible to regulators and stakeholders. Circularity and recovery in mining can be understood as strategies that aim to reduce virgin inputs and residual outputs by keeping materials, water, and value in productive use for longer. This orientation is consistent with circular economy definitions that emphasize maintaining materials at high utility and value while reducing waste generation (Stahel, 2016). In mining systems, circularity is implemented through changes to extraction, processing, water management, and downstream reuse pathways, including cross-industry exchanges that resemble industrial symbiosis and eco-industrial development (Chertow, 2000; Geetha, 2025). From a systems perspective, these strategies create technical opportunities but also introduce implementation challenges related to integration, risk, compliance, and performance stability. Although research on mining circularity has expanded rapidly since 2000, it remains fragmented across waste streams, process routes, and performance definitions. Many studies report strong process-level results, but comparison across studies is difficult because papers often use different metrics and units (for example, recovery versus removal efficiency), define performance over different system scopes (single unit operation versus an integrated process train), and report different sets of indicators (for example, technical recovery without consistent operational or economic metrics). Typical reporting is strongest at the process level, where studies commonly quantify recovery yield, selectivity, reagent and energy intensity, process time and productivity, operational stability, and by-product handling (Kaya et al., 2020; Mourdikoudis & Dominguez-Benetton, 2025). These indicators are useful for demonstrating technical feasibility, but they do not, by themselves, show whether a pathway can be integrated into an operating plant and maintained under realistic variability. As a result, traceability from technical improvements to operational outcomes, such as reliability under variable feed conditions or integration into existing processing flowsheets, is uneven, and traceability to economic outcomes such as operating cost per ton, margin per ton, or risk-adjusted viability is even less consistent (Khan & Magweregwede, 2025; Mulopo, 2022). This creates a gap between process evidence and decision-ready evidence for adoption and scale-up. As a result, the literature contains many strong technical contributions, but fewer studies that make it clear how a recovery route becomes a stable plant-level practice with repeatable performance. A key reason for this gap is that moving to plant-scale implementation is both a technical and an organizational challenge. Beyond proving that a recovery mechanism works, implementation requires coordination across functions, disciplined execution, monitoring and control routines, knowledge capture across shifts and contractors, and learning cycles that
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